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Cognitive Computation

, Volume 11, Issue 2, pp 317–327 | Cite as

Hierarchical Neural Representation for Document Classification

  • Jianming Zheng
  • Fei CaiEmail author
  • Wanyu Chen
  • Chong Feng
  • Honghui Chen
Article
  • 157 Downloads

Abstract

Text representation, which converts text spans into real-valued vectors or matrices, is a crucial tool for machines to understand the semantics of text. Although most previous works employed classic methods based on statistics and neural networks, such methods might suffer from data sparsity and insensitivity to the text structure, respectively. To address the above drawbacks, we propose a general and structure-sensitive framework, i.e., the hierarchical architecture. Specifically, we incorporate the hierarchical architecture into three existing neural network models for document representation, thereby producing three new representation models for document classification, i.e., TextHFT, TextHRNN, and TextHCNN. Our comprehensive experimental results on two public datasets demonstrate the effectiveness of the hierarchical architecture. With a comparable (or substantially less) time expense, our proposals obtain significant improvements ranging from 4.65 to 35.08% in terms of accuracy against the baseline. We can conclude that the hierarchical architecture can enhance the classification performance. In addition, we find that the benefits provided by the hierarchical architecture can be strengthened as the document length increases.

Keywords

Document representation Neural networks Hierarchical architecture Document classification 

Notes

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Informed Consent

Informed consent was not required as no human or animals were involved.

Human and Animal Rights

This article does not contain any studies with human participants performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Jianming Zheng
    • 1
  • Fei Cai
    • 1
    Email author
  • Wanyu Chen
    • 1
  • Chong Feng
    • 1
  • Honghui Chen
    • 1
  1. 1.Science and Technology on Information Systems Engineering LaboratoryNational University of Defense TechnologyChangshaChina

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